A nested case-control study of mammographic patterns, breast volume, and breast cancer (New York City, NY, United States)

Role of Ferroptosis in Stroke

Stroke is a common and serious nervous system disease caused by the rupture or blockage of the cardiovascular system. It causes millions of deaths and disabilities every year, which is a huge burden on humanity. It may be induced by thrombosis, hypertension, hyperlipidemia, hyperglycemia, smoking, advanced age and so on. According to different causes, stroke can be generally divided into hemorrhagic stroke and ischemic stroke, whose pathogenesis and treatment are quite different. Ferroptosis is a new type of cell death first defined in 2012, which is characterized by non-apoptotic, iron-dependent, and over-accumulated lipid peroxides. Excess lipid reactive oxygen species produced during ferroptosis eventually leads to oxidative cell death. Ferroptosis has been shown to occur and play an important role in tumors, neurological diseases, kidney injury, and ischemia-reperfusion injury. Ferroptosis is also closely related to the pathogenesis of stroke. Moreover, scientists have successfully intervened in the process of stroke in animal models by regulating ferroptosis, indicating that ferroptosis is a new potential target for the treatment of stroke. This paper systematically summarizes the involvement and role of ferroptosis in the pathogenesis of stroke and predicts the potential of ferroptosis in the treatment of stroke. Ferroptosis in stroke. Stroke induces iron overload and lipid metabolism disorders. Elevated iron catalyzes lipid peroxidation and eventually triggers ferroptosis. Conversely, the GSH/GPX4 pathway, as well as CoQ10, Fer-1, and Lip-1, inhibits lipid peroxidation and, thus, alleviates ferroptosis. GSH glutathione; GPX4 glutathione peroxidase 4; CoQ10 coenzyme Q10; Lip-1 liproxstatin-1; Fer-1 ferostatin-1.

A randomized controlled trial of metformin in women with components of metabolic syndrome: intervention feasibility and effects on adiposity and breast density

PURPOSE

Obesity is a known risk factor for post-menopausal breast cancer and may increase risk for triple negative breast cancer in premenopausal women. Intervention strategies are clearly needed to reduce obesity-associated breast cancer risk.

METHODS

We conducted a Phase II double-blind, randomized, placebo-controlled trial of metformin in overweight/obese premenopausal women with components of metabolic syndrome to assess the potential of metformin for primary breast cancer prevention. Eligible participants were randomized to receive metformin (850 mg BID, n = 76) or placebo (n = 75) for 12 months. Outcomes included breast density, assessed by fat/water MRI with change in percent breast density as the primary endpoint, anthropometric measures, and intervention feasibility.

RESULTS

Seventy-six percent in the metformin arm and 83% in the placebo arm (p = 0.182) completed the 12-month intervention. Adherence to study agent was high with more than 80% of participants taking ≥ 80% assigned pills. The most common adverse events reported in the metformin arm were gastrointestinal in nature and subsided over time. Compared to placebo, metformin intervention led to a significant reduction in waist circumference (p < 0.001) and waist-to-hip ratio (p = 0.019). Compared to placebo, metformin did not change percent breast density and dense breast volume but led to a numerical but not significant decrease in non-dense breast volume (p = 0.070).

CONCLUSION

We conclude that metformin intervention resulted in favorable changes in anthropometric measures of adiposity and a borderline decrease in non-dense breast volume in women with metabolic dysregulation. More research is needed to understand the impact of metformin on breast cancer risk reduction.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02028221. Registered January 7, 2014, https://clinicaltrials.gov/ct2/show/NCT02028221.

Effect of Vitamin D Supplementation on Risk of Breast Cancer: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Objective: Laboratory findings indicated that vitamin D might have a potent protective effect on breast cancer, but epidemiology studies reported conflicting results. The aim of the study was to conduct a systematic review and meta-analysis to clarify the efficacy of vitamin D supplementation on risk of breast cancer.

Methods: MEDLINE, EMBASE, The Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and abstracts of three major conferences were searched (up to December 8, 2020). Parallel randomized controlled trials (RCTs) examining the efficacy of vitamin D supplementation on risk of breast cancer or change of mammography compared with placebo in females were included. Data were meta-analyzed using a random-effects model. Bayesian meta-analysis was conducted to synthesize the results using data from observational studies as priors.

Results: Seven RCTs were identified for effect of vitamin D on risk of breast cancer, with 19,137 females included for meta-analysis. No statistically significant effect of vitamin D on risk of breast cancer was found in classical random-effects meta-analysis (risk ratio = 1.04, 95% confidence interval: 0.84–1.28, p = 0.71). When Bayesian meta-analyses were conducted, results remained non-significant. There was no statistically significant effect of vitamin D on mammography density observed: mean difference = 0.46, 95% confidence interval: −2.06 to 2.98, p = 0.72.

Conclusion: There is insufficient evidence to support the efficacy of vitamin D supplementation in breast cancer risk and change of mammography density. The protective effect of vitamin D on risk of breast cancer from previous observational studies may be overestimated.

Systematic Review Registration: PROSPERO, identifier CRD42019138718.

Association of Interactions Between Mammographic Density Phenotypes and Established Risk Factors With Breast Cancer Risk, by Tumor Subtype and Menopausal Status

Previous studies suggest that the association between mammographic density (MD) and breast cancer risk might be modified by other breast cancer risk factors. In this study, we assessed multiplicative interactions between MD measures and established risk factors on the risk of invasive breast cancer overall and according to menopausal and estrogen receptor status. We used data on 2,137 cases and 4,346 controls from a nested case-control study within the Nurses' Health Study (1976-2004) and Nurses' Health Study II (1989-2007), whose data on percent mammographic density (PMD) and absolute area of dense tissue and nondense tissue (NDA) were available. No interaction remained statistically significant after adjusting for number of comparisons. For breast cancer overall, we observed nominally significant interactions (P < 0.05) between nulliparity and PMD/NDA, age at menarche and area of dense tissue, and body mass index and NDA. Individual nominally significant interactions across MD measures and risk factors were also observed in analyses stratified by either menopausal or estrogen receptor status. Our findings help provide further insights into potential mechanisms underlying the association between MD and breast cancer.

In utero DDT exposure and breast density before age 50

Prior studies in the Child Health and Development Studies (CHDS) found in utero exposure to the pesticide, dichlorodiphenyltrichloroethane (DDT), increased breast cancer risk by age 52. Mammographic density is considered a primary risk factor for breast cancer. We conducted a study of 309 daughters from the CHDS to examine in utero DDT exposure and mammographic density in midlife. Among daughters with high (>75th percentile) exposure to p,p'-Dichlorodiphenyldichloroethylene (DDE), p,p'-DDT was significantly correlated with increased dense area and percent density regardless of her body mass in midlife. In the subset of women with lower (<75th percentile) p,p-DDE, p,p'-DDT was associated with increased non-dense breast area. This was explained by adjustment for midlife BMI suggesting that p,p'-DDT may be obesogenic. In aggregate our findings indicate that early life p,p'-DDT exposure impacts breast density in a complex way that depends on the hosts biological ability to sequester and process DDT and levels of exposure.

Diffuse optical tomography of the breast: a potential modifiable biomarker of breast cancer risk with neoadjuvant chemotherapy

The purpose of this study is to evaluate whether a diffuse optical tomography breast imaging system (DOTBIS) can provide a comparable optical-based image index of mammographic breast density, an established biomarker of breast cancer risk. Oxyhemoglobin concentration (ctO₂Hb) measured by DOTBIS was collected from 40 patients with stage II-III breast cancer. The tumor-free contralateral breast was used for this evaluation. We observed a moderate positive correlation between the patient's mammogram density classification and ctO₂Hb, r_s = 0.486 (p = 0.001). In addition, significant reduction in ctO₂Hb levels were noted during neoadjuvant chemotherapy treatment (p = 0.017). This observation indicates that ctO₂Hb levels measured by DOTBIS could be a novel modifiable imaging biomarker of breast cancer risk and warrants further investigation.

Determinants of Mammographic Density Change

Background

Mammographic density (MD) is a strong risk factor for breast cancer. We examined how breast cancer risk factors are associated with MD area (cm²) change across age.

Methods

We conducted a cohort study of 31 782 Swedish women ages 40–70 years at time of baseline mammogram. Lifestyle and reproductive risk factors were assessed by a web-based questionnaire. MD was measured as dense area using the STRATUS method (mean over the left and right breast). Linear regression analyses with adjustments for age, body mass index (BMI), and menopausal status at baseline were performed to assess the association between breast cancer risk factors and mean baseline MD. To investigate mean MD change across age, linear regression analyses with adjustments for age, BMI, menopausal status, and age at last mammogram were performed. All tests of statistical significance were two-sided.

Results

Except for oral contraceptive use, established lifestyle and reproductive risk factors for breast cancer were associated with baseline mean MD. The overall average annual MD change was −1.0 cm². BMI and physical activity were statistically significantly associated with MD change. Lean women (BMI <20 kg/m²) had a mean MD change of −1.13 cm² per year (95% confidence interval = −1.25 to −1.02) compared with −0.46 cm² per year (95% confidence interval = −0.57 to −0.35) for women with BMI 30 or higher. The annual MD change was −0.4 cm² larger in women who were very physically active compared with less physically active women.

Conclusions

Our results indicate that all risk factors for breast cancer, except oral contraceptive use, are associated with baseline MD but that only age, BMI, and physical activity are determinants of MD change.

Factors of Risk for Breast Cancer Influencing Post-Menopausal Long-Term Hormone Replacement Therapy

The advantages of hormone replacement therapy (HRT) are well documented in contrasting the symptomatology of climacterium and in reducing morbidity and mortality associated with coronary heart disease and osteoporotic fractures of postmenopausal age. However, growing evidence points to increased breast cancer risk in HRT long-term users, and the adverse effect would, obviously, overwhelm any other benefit. At present, the risk/benefit ratio of HRT is an object of hot debate, and we feel it necessary and urgent to select women who can safely benefit from HRT and women whose risk of breast cancer can be perilously increased by the raised hormonal levels related to HRT. We have reviewed studies on the breast cancer risk in HRT users and data on the interaction between steroid hormones and breast cancer. Reasoning that the outcome of mammary cancer can be increased by hormonal overstimulation of the breast, we have focused on those factors of risk that could be further enhanced by the exogenous hormonal stimulus of HRT, so as to cause a further significant increase in the risk of breast cancer. We conclude that some biologic and clinical markers, namely android obesity, bone density, mammographic density, androgen and estrogen circulating levels, alcohol consumption, benign breast disease, and familiarity, should be carefully considered before prescribing long-term HRT. Our analysis suggests that HRT could increase the risk of breast cancer and useless in preventing coronary heart disease and osteoporotic fractures when administered in women with positivity for one or more of these markers.

An overview of mammographic density and its association with breast cancer

In 2017, breast cancer became the most commonly diagnosed cancer among women in the US. After lung cancer, breast cancer is the leading cause of cancer-related mortality in women. The breast consists of several components, including milk storage glands, milk ducts made of epithelial cells, adipose tissue, and stromal tissue. Mammographic density (MD) is based on the proportion of stromal, epithelial, and adipose tissue. Women with high MD have more stromal and epithelial cells and less fatty adipose tissue, and are more likely to develop breast cancer in their lifetime compared to women with low MD. Because of this correlation, high MD is an independent risk factor for breast cancer. Further, mammographic screening is less effective in detecting suspicious lesions in dense breast tissue, which can lead to late-stage diagnosis. Molecular differences between dense and non-dense breast tissues explain the underlying biological reasons for why women with dense breasts are at a higher risk for developing breast cancer. The goal of this review is to highlight the current molecular understanding of MD, its association with breast cancer risk, the demographics pertaining to MD, and the environmental factors that modulate MD. Finally, we will review the current legislation regarding the disclosure of MD on a traditional screening mammogram and the supplemental screening options available to women with dense breast tissue.

Non-randomness of the anatomical distribution of tumors

Background

Why does a tumor start where it does within an organ? Location is traditionally viewed as a random event, yet the statistics of the location of tumors argues against this being a random occurrence. There are numerous examples including that of breast cancer. More than half of invasive breast cancer tumors start in the upper outer quadrant of the breast near the armpit, even though it is estimated that only 35 to 40% of breast tissue is in this quadrant. This suggests that there is an unknown microenvironmental factor that significantly increases the risk of cancer in a spatial manner and that is not solely due to genes or toxins. We hypothesize that tumors are more prone to form in healthy tissue at microvascular ‘hot spots’ where there is a high local concentration of microvessels providing an increased blood flow that ensures an ample supply of oxygen, nutrients, and receptors for growth factors that promote the generation of new blood vessels.

Results

To show the plausibility of our hypothesis, we calculated the fractional probability that there is at least one microvascular hot spot in each region of the breast assuming a Poisson distribution of microvessels in two-dimensional cross sections of breast tissue. We modulated the microvessel density in various regions of the breast according to the total hemoglobin concentration measured by near infrared diffuse optical spectroscopy in different regions of the breast. Defining a hot spot to be a circle of radius 200 μm with at least 5 microvessels, and using a previously measured mean microvessel density of 1 microvessel/mm², we find good agreement of the fractional probability of at least one hot spot in different regions of the breast with the observed invasive tumor occurrence. However, there is no reason to believe that the microvascular distribution obeys a Poisson distribution.

Conclusions

The spatial location of a tumor in an organ is not entirely random, indicating an unknown risk factor. Much work needs to be done to understand why a tumor occurs where it does.

Electronic supplementary material

The online version of this article (10.1186/s41236-017-0006-7) contains supplementary material, which is available to authorized users.

Adiposity, breast density, and breast cancer risk: epidemiological and biological considerations

Excess total body fat and abdominal adipose tissue are recognized risk factors for metabolic diseases but also for some types of cancers, including breast cancer. Several biological mechanisms in connection with local and systemic effects of adiposity are believed to be implicated in breast cancer development, and may involve breast fat. Breast adipose tissue can be studied through mammography by looking at breast density features such as the nondense area mainly composed of fat, or the percent breast density, which is the proportion of fibroglandular tissue in relation to fat. The relation between adiposity, breast density features, and breast cancer is complex. Studies suggest a paradoxical association as adiposity and absolute nondense area correlate positively with each other, but in contrast to adiposity, absolute nondense area seems to be associated negatively with breast cancer risk. As breast density is one of the strongest risk factors for breast cancer, it is therefore critical to understand how these factors interrelate. In this review, we discuss these relations by first presenting how adiposity measurements and breast density features are linked to breast cancer risk. Then, we used a systematic approach to capture the literature to review the relation between adiposity and breast density features. Finally, the role of adipose tissue in carcinogenesis is discussed briefly from a biological perspective.

Percent mammographic density prediction: development of a model in the nurses' health studies

PURPOSE

To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk.

METHODS

Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r ² in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk.

RESULTS

In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both).

CONCLUSION

These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.

Quantitative Assessment of Percent Breast Density: Analog versus Digital Acquisition

Breast density is a moderate risk factor for breast cancer based on quantitative measurement of percent breast density from film-screen mammograms. In this study, percent breast density was determined using computer-assisted interactive thresholding software from sixty consecutive mammograms of women undergoing digital screening mammography with a prior film-screen mammogram obtained within the last two years. Observations were made regarding discrepancies in density readings. Percent breast density was significantly lower for digital mammograms (mean 32.2%) compared to analog mammograms (mean 40.3%) (p<0.0001). This was not significant for women with less than 20% breast density (range +0.3 to −2.7%), but larger differences were seen with increasing density (12.5–14.9% lower for >50% density). Differences in density readings between analog and digital mammography were largely observed to be due to better recognition of the skin line on digital mammograms resulting in inclusion of more subcutaneous fat. Difficulties with appropriate recognition of subcutaneous breast tissue and fatty tissue near the chest wall were present for both analog and digital mammography. In conclusion, percent breast density is significantly lower when the mammogram is acquired in digital format compared to film-screen, largely due to better recognition of the skin line with resultant inclusion of more subcutaneous fat. Breast cancer risk predictions based on computerized assessment of breast density may be underestimated when applied to digital mammography.

Relation between Mammographic Parenchymal Patterns and Breast Cancer Risk: Considering BMI, Compressed Breast Thickness and Age of Women in Tabriz, Iran

BACKGROUND

Mammographic density determined according paranchymal patterns is a risk factor for breast cancer and its relationships with body and other breast characteristics of women is important. The purpose of the present study was to correlate breast parenchymal patterns and mammography abnormality findings with women's BMI, compressed breast thickness (CBT) and age in Tabriz city, Iran.

MATERIALS AND METHODS

From 1,100 mammograms interpreted by radiologists, breast parenchymal was classified into four categories from Types 1 (mostly fatty) through 4 (mostly fibroglandular tissue). Age, BMI, and CBT were recorded and their relation with risk for the development of breast abnormalities in mammograms was analyzed.

RESULTS

In women with a mean age of 45.8±8.63 years 17.7% were in the high density group (Type 3 and 4). A comparison of four types of breast paranchymal with BMI, CBT and age showed inverse relations to breast density. Abnormal mammographic findings were 25.8% of all reported mammograms with a circular mass (12.7%) as the most common abnormality. About 21% abnormal cases were in less than 40 years. Increasing of BMI had significant relation with breast abnormality but in CBT was not observed.

CONCLUSIONS

Measurement of women's body characteristics is useful for assistance in mammography diagnosis as well as selection of imaging instrument by high sensitivity for following patient in future. The effects of age, CBT and BMI groups on the breast paranchymal were significant.

Evaluation of breast parenchymal density with QUANTRA software

PURPOSE

To evaluate breast parenchymal density using QUANTRA software and to correlate numerical breast density values obtained from QUANTRA with ACR BI-RADS breast density categories.

MATERIALS AND METHODS

Two-view digital mammograms of 545 consecutive women (mean age - 47.7 years) were categorized visually by three independent radiologists into one of the four ACR BI-RADS categories (D1-D4). Numerical breast density values as obtained by QUANTRA software were then used to establish the cutoff values for each category using receiver operator characteristic (ROC) analysis.

RESULTS

Numerical breast density values obtained by QUANTRA (range - 7-42%) were systematically lower than visual estimates. QUANTRA breast density value of less than 14.5% could accurately differentiate category D1 from the categories D2, D3, and D4 [area under curve (AUC) on ROC analysis - 94.09%, sensitivity - 85.71%, specificity - 84.21%]. QUANTRA density values of <19.5% accurately differentiated categories D1 and D2 from D3 and D4 (AUC - 94.4%, sensitivity - 87.50%, specificity - 84.60%); QUANTRA density values of <26.5% accurately differentiated categories D1, D2, and D3 from category D4 (AUC - 90.75%, sensitivity - 88.89%, specificity - 88.621%).

CONCLUSIONS

Breast density values obtained by QUANTRA software can be used to obtain objective cutoff values for each ACR BI-RADS breast density category. Although the numerical density values obtained by QUANTRA are lower than visual estimates, they correlate well with the BI-RADS breast density categories assigned visually to the mammograms.

Anthropometric factors, adult weight gain, and mammographic features

PURPOSE

To evaluate the association between anthropometric factors, weight gain during adulthood, and mammographic features among 1,435 women recruited at screening mammography.

METHODS

Spearman's partial coefficients were used to evaluate the correlation of anthropometric factors with mammographic features (percent density, absolute dense area, and non-dense area). Multivariate generalized linear models were used to evaluate the associations between weight change categories and mammographic features.

RESULTS

Body mass index was inversely correlated with percent density (r = -0.49, p < 0.0001) or absolute dense area (r = -0.21, p < 0.0001) and positively correlated with absolute non-dense area (r = 0.69, p < 0.0001). However, body mass index was positively correlated with absolute dense area when adjusting for absolute non-dense area (r = 0.16, p < 0.0001). Similar results were observed for weight, waist circumference, and waist-to-hip ratio with mammographic features. Within increasing categories of weight change, percent density (p trend < 0.0001) and absolute dense area (p trend = 0.025) increased, while absolute non-dense area decreased (p trend < 0.0001). After stratification by the median of non-dense area, the positive association between weight gain and absolute dense area remained only among women with higher non-dense area.

CONCLUSIONS

Adiposity seems positively associated with both dense and non-dense areas following adjustment for each other. Our findings suggest a higher breast dense area among women who gained weight and that a minimum of breast fat may be needed to promote the proliferation of this fibroglandular tissue.

Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction

Introduction

Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.

Methods

We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.

Results

During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm², 21-30cm², 31-40cm², 41-50cm², 51-60cm², and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.

Conclusion

Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.

The relationship between breast density and bone mineral density after menopause

[Purpose] The purpose of this study was to analyze the relationship between breast density and bone mineral density after menopause. [Subjects and Methods] The subjects were 130 patients who participated in a bone densitometry test and had a mammogram taken between January 1st, 2013 to October 1st, 2014. The mammograms were scored breast imaging-reporting and data system. Grade 1 indicates almost only fat, Grade 2 indicates fibroglandular densities, Grade 3 indicates heterogeneously dense tissue, and Grade 4 indicates an extreme density. Correlation analysis was carried out to investigate the relationship between breast density grades and bone mineral densities by age and body mass index. [Results] Breast density had a close relationship with age (−0.59), Body mass index (−0.39), and T-score (0.29). The results indicate that as age and body mass index increase, the grade of the breast density decreases, and as the T-score increases, the grade increases. [Conclusion] A precise evaluation of the of breast cancer risk associated with breast density should be conducted as a large scale prospective study for women in Korea.

The association between breast tissue optical content and mammographic density in pre- and post-menopausal women

Mammographic density (MD), associated with higher water and lower fat content in the breast, is strongly related to breast cancer risk. Optical attenuation spectroscopy (OS) is a non-imaging method of evaluating breast tissue composition by red and near-infrared light transmitted through the breast that, unlike mammography, does not involve radiation. OS provides information on wavelength dependent light scattering of tissue and on absorption by water, lipid, oxy-, deoxy-hemoglobin. We propose that OS could be an alternative marker of breast cancer risk and that OS breast tissue measures will be associated with MD. In the present analysis, we developed an algorithm to estimate breast tissue composition and light scattering parameters using a spectrally constrained global fitting procedure employing a diffuse light transport model. OS measurements were obtained from 202 pre- and post-menopausal women with normal mammograms. Percent density (PD) and dense area (DA) were measured using Cumulus. The association between OS tissue composition and PD and DA was analyzed using linear regression adjusted for body mass index. Among pre-menopausal women, lipid content was significantly inversely associated with square root transformed PD (β = -0.05, p = 0.0002) and DA (β = -0.05, p = 0.019); water content was significantly positively associated with PD (β = 0.06, p = 0.008). Tissue oxygen saturation was marginally inversely associated with PD (β = -0.03, p = 0.057) but significantly inversely associated with DA (β = -0.10, p = 0.002). Among post-menopausal women lipid and water content were significantly associated (negatively and positively, respectively) with PD (β_lipid = -0.08, β_water = 0.14, both p<0.0001) and DA (β_lipid = -0.10, p<0.0001; β_water = 0.11, p = 0.001). The association between OS breast content and PD and DA is consistent with more proliferation in dense tissue of younger women, greater lipid content in low density tissue and higher water content in high density tissue. OS may be useful for assessing physiologic tissue differences related to breast cancer risk, particularly when mammography is not feasible or easily accessible.

A level set based framework for quantitative evaluation of breast tissue density from MRI data

Breast density is a risk factor associated with the development of breast cancer. Usually, breast density is assessed on two dimensional (2D) mammograms using the American College of Radiology (ACR) classification. Magnetic resonance imaging (MRI) is a non-radiation based examination method, which offers a three dimensional (3D) alternative to classical 2D mammograms. We propose a new framework for automated breast density calculation on MRI data. Our framework consists of three steps. First, a recently developed method for simultaneous intensity inhomogeneity correction and breast tissue and parenchyma segmentation is applied. Second, the obtained breast component is extracted, and the breast-air and breast-body boundaries are refined. Finally, the fibroglandular/parenchymal tissue volume is extracted from the breast volume. The framework was tested on 37 randomly selected MR mammographies. All images were acquired on a 1.5T MR scanner using an axial, T1-weighted time-resolved angiography with stochastic trajectories sequence. The results were compared to manually obtained groundtruth. Dice's Similarity Coefficient (DSC) as well as Bland-Altman plots were used as the main tools for evaluation of similarity between automatic and manual segmentations. The average Dice's Similarity Coefficient values were and for breast and parenchymal volumes, respectively. Bland-Altman plots showed the mean bias () standard deviation equal for breast volumes and for parenchyma volumes. The automated framework produced sufficient results and has the potential to be applied for the analysis of breast volume and breast density of numerous data in clinical and research settings.

Breast size and breast cancer: a systematic review

BACKGROUND

There are many known breast cancer risk factors, but traditionally the list has not included breast size. The aim of this study was to synthesize the literature on breast size as a risk factor for breast carcinoma by examining studies addressing this question both directly and indirectly.

METHODS

A systematic review was performed searching MEDLINE from 1950 to November 2010, and updated again in February 2014. Literature was sought to assess the relationship between the following variables and breast cancer: 1) breast size; 2) breast reduction; 3) breast augmentation; and 4) prophylactic subcutaneous mastectomy. Findings were summarized and the levels of evidence were assessed.

RESULTS

50 papers were included in the systematic review. Increasing breast size appears to be a risk factor for breast cancer, but studies are limited by their retrospective nature, imperfect size measurement techniques and confounding variables. The evidence is stronger for risk reduction with breast reduction, including prophylactic subcutaneous mastectomy at the extreme. Generally the breast augmentation population has a lower risk of breast cancer than the general population, but it is unclear whether or not this is related to the bias of small breasts in this patient population and the presence of other confounders.

CONCLUSIONS

There is direct and indirect evidence that breast size is an important factor in the risk of developing breast cancer. Plastic surgeons are in a unique position to observe this effect. Well-designed prospective studies are required to further assess this risk factor.

Association between mammographic density and basal-like and luminal A breast cancer subtypes

Introduction

Mammographic density is a strong risk factor for breast cancer overall, but few studies have examined the association between mammographic density and specific subtypes of breast cancer, especially aggressive basal-like breast cancers. Because basal-like breast cancers are less frequently screen-detected, it is important to understand how mammographic density relates to risk of basal-like breast cancer.

Methods

We estimated associations between mammographic density and breast cancer risk according to breast cancer subtype. Cases and controls were participants in the Carolina Breast Cancer Study (CBCS) who also had mammograms recorded in the Carolina Mammography Registry (CMR). A total of 491 cases had mammograms within five years prior to and one year after diagnosis and 528 controls had screening or diagnostic mammograms close to the dates of selection into CBCS. Mammographic density was reported to the CMR using Breast Imaging Reporting and Data System categories. The expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 1 and 2 (HER1 and HER2), and cytokeratin 5/6 (CK5/6) were assessed by immunohistochemistry and dichotomized as positive or negative, with ER+ and/or PR+, and HER2- tumors classified as luminal A and ER-, PR-, HER2-, HER1+ and/or CK5/6+ tumors classified as basal-like breast cancer. Triple negative tumors were defined as negative for ER, PR and HER2. Of the 491 cases 175 were missing information on subtypes; the remaining cases included 181 luminal A, 17 luminal B, 48 basal-like, 29 ER-/PR-/HER2+, and 41 unclassified subtypes. Odds ratios comparing each subtype to all controls and case-case odds ratios comparing mammographic density distributions in basal-like to luminal A breast cancers were estimated using logistic regression.

Results

Mammographic density was associated with increased risk of both luminal A and basal-like breast cancers, although estimates were imprecise. The magnitude of the odds ratio associated with mammographic density was not substantially different between basal-like and luminal A cancers in case–control analyses and case-case analyses (case-case OR = 1.08 (95% confidence interval: 0.30, 3.84)).

Conclusions

These results suggest that risk estimates associated with mammographic density are not distinct for separate breast cancer subtypes (basal-like/triple negative vs. luminal A breast cancers). Studies with a larger number of basal-like breast cancers are needed to confirm our findings.

Prenatal smoke exposure and mammographic density in mid-life

Tobacco smoke has both carcinogenic effects and anti-estrogenic properties and its inconsistent association with breast cancer risk in observational studies may be because of these competing effects across the lifecourse. We conducted a prospective study of prenatal smoke exposure, childhood household smoke exposure, and adult active smoke exposure and mammographic density, a strong intermediate marker of breast cancer risk, in an adult follow-up of existing US birth cohorts. Specifically, we followed up women who were born between 1959 and 1967 and whose mothers participated in either the Collaborative Perinatal Project (Boston and Providence sites) or the Childhood Health and Development Study in California. Of the 1134 women interviewed in adulthood (ranging in age from 39 to 49 years at interview), 79% had a screening mammogram. Cigarette smoking was reported by mothers at the time of their pregnancy; 40% of mothers smoked while pregnant. Women whose mothers smoked during pregnancy had a 3.1% (95% confidence interval (CI) = -6.0%, -0.2%) lower mammographic density than women whose mothers did not smoke during pregnancy. When we further accounted for adult body mass index and adult smoking status, the association remained (β = -2.7, 95% CI = -5.0, -0.3). When we examined patterns of smoking, prenatal smoke exposure without adult smoke exposure was associated with a 5.6% decrease in mammographic density (β = -5.6, 95% CI = -9.6, -1.6). Given the strength of mammographic density as an intermediate marker for breast cancer, the inverse associations between mammographic density and smoking patterns across the lifecourse may help explain the complex association between cigarette smoking and breast cancer risk.

Mammographic density is not a worthwhile examination to distinguish high cancer risk women in screening

Numerous studies established high mammographic density (MD) as a significant breast cancer risk. By adopting both radiological and epidemiological perspectives, we analysed the capacity of this radiological parameter to express an individual level of risk and the methods for assessing the relationship between MD categories and risk. MD is unable to identify individual underlying anatomical and physiological components. Many factors affect accurate and reproducible measurements and consequently classifications of MD. Significant relative risks were found by comparing the MD categories in the tails of distribution (i.e. the group of women with the lowest MD to that with the highest MD), which represent <10 % of women in each group: the majority of the population was ignored. When a relevant threshold of MD was applied to compare another group and the entire population was included to compare the two groups, some studies showed no significant or only moderate relative risk (RR) between women with readings above and those below the threshold. Sensitivity and specificity remain unknown. MD cannot be considered a worthwhile test by which to categorically identify high-risk women in screening. Key points • Unknown individual anatomical and physiological components do not express the risk level.• The epidemiological conditions are not relevant to distinguish a high-risk category.• The most relevant studies show no or moderate risks.

Reliability of the percent density in digital mammography with a semi-automated thresholding method

PURPOSE

The reliability of the quantitative measurement of breast density with a semi-automated thresholding method (Cumulus™) has mainly been investigated with film mammograms. This study aimed to evaluate the intrarater reproducibility of percent density (PD) by Cumulus™ with digital mammograms.

METHODS

This study included 1,496 craniocaudal digital mammograms from the unaffected breast of breast cancer patients. One rater reviewed each mammogram and estimated the PD using the Cumulus™ method. All images were reassessed by the same rater 1 month later without reference to the previously assigned values. The repeatability of the PD was evaluated by an intraclass correlation coefficient (ICC). All patients were grouped based on their body mass index (BMI), age, family history of breast cancer, breastfeeding history and breast area (calculated with Cumulus™), and subgroup analysis for the ICC of each group was performed. All patients were categorized by their Breast Imaging Reporting and Data System (BI-RADS) density pattern, and the mean and standard deviation of the PD by each BI-RADS categories were compared.

RESULTS

The ICC for the PD was 0.94, indicating excellent repeatability. The discrepancy between the paired PD values ranged from 0 to 23.93, with an average of 3.90 (standard deviation=3.39). The subgroup ICCs for the PD ranged from 0.88 to 0.96, indicating excellent reliability in all subgroups regardless of patient variables. The ICCs of the PD for the high-risk (BI-RADS 3 and 4) and low-risk (BI-RADS 1 and 2) groups were 0.90 and 0.88, respectively.

CONCLUSION

This study suggests that PD calculated with digital mammograms has an acceptable reliability regardless of patient age, BMI, family history of breast cancer, breastfeeding history, breast size, and BI-RADS density pattern.

Differential impact of body mass index on absolute and percent breast density: implications regarding their use as breast cancer risk biomarkers

Percent breast density (PBD), a commonly used biomarker of breast cancer risk (BCR), is confounded by the influence of non-dense breast tissue on its measurement and factors, such as BMI, which have an impact on non-dense tissue. Consequently, BMI, a potent BCR factor, is, paradoxically, negatively correlated with PBD. We propose that absolute breast density (ABD) is a more accurate biomarker of BCR. We used a volumetric method to compare the correlation between PBD and ABD with baseline demographics and dietary and physical activity variables in a group of 169 postmenopausal women enrolled in a clinical trial prior to any intervention. As expected, a strong negative correlation between PBD and BMI was observed (Rho = -0.5, p < 5e(-12)). In contrast, we observed a strong, previously not well established, positive correlation of BMI with ABD (Rho = 0.41, p < 2.5e(-8)), which supports the use of ABD as a more accurate indicator of BCR. Correction of PBD by BMI did not frequently provide the same information as ABD. In addition, because of the strong influence of BMI on ABD, many correlations between dietary variables and ABD did not emerge, until adjustment was made for BMI. ABD corrected by BMI should be the gold standard BD measurement. These findings identify the optimal measurement of BD when testing the influence of an intervention on BD as a biomarker of BCR.

Mammographic density phenotypes and risk of breast cancer: a meta-analysis

BACKGROUND

Fibroglandular breast tissue appears dense on mammogram, whereas fat appears nondense. It is unclear whether absolute or percentage dense area more strongly predicts breast cancer risk and whether absolute nondense area is independently associated with risk.

METHODS

We conducted a meta-analysis of 13 case-control studies providing results from logistic regressions for associations between one standard deviation (SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects models to calculate pooled odds ratios and 95% confidence intervals (CIs). All tests were two-sided with P less than .05 considered to be statistically significant.

RESULTS

Among premenopausal women (n = 1776 case patients; n = 2834 control subjects), summary odds ratios were 1.37 (95% CI = 1.29 to 1.47) for absolute dense area, 0.78 (95% CI = 0.71 to 0.86) for absolute nondense area, and 1.52 (95% CI = 1.39 to 1.66) for percentage dense area when pooling estimates adjusted for age, body mass index, and parity. Corresponding odds ratios among postmenopausal women (n = 6643 case patients; n = 11187 control subjects) were 1.38 (95% CI = 1.31 to 1.44), 0.79 (95% CI = 0.73 to 0.85), and 1.53 (95% CI = 1.44 to 1.64). After additional adjustment for absolute dense area, associations between absolute nondense area and breast cancer became attenuated or null in several studies and summary odds ratios became 0.82 (95% CI = 0.71 to 0.94; P heterogeneity = .02) for premenopausal and 0.85 (95% CI = 0.75 to 0.96; P heterogeneity < .01) for postmenopausal women.

CONCLUSIONS

The results suggest that percentage dense area is a stronger breast cancer risk factor than absolute dense area. Absolute nondense area was inversely associated with breast cancer risk, but it is unclear whether the association is independent of absolute dense area.

Changes in mammographic density over time in breast cancer cases and women at high risk for breast cancer

High mammographic breast density is one of the strongest intermediate markers of breast cancer risk, and decreases in density over time have been associated with decreases in breast cancer risk. Using repeated measures of mammographic density in a cohort of high-risk women, the Women at Risk (WAR) cohort at Columbia University Medical Center (N = 2670), we examined whether changes in prediagnostic mammographic density differed among 85 prospectively-ascertained breast cancer cases and 85 age-matched controls, using a nested case–control design. Median age at first mammogram was 51 years (range, 29–77 years), with a median of 4 years between first and second prediagnostic mammogram (range, 1–15 years). Using linear regression with change in percent density as the outcome, we found that in women who did not go on to be diagnosed with breast cancer, change in percent density decreased as time between first and second mammogram increased (β = −1.62% per year, p = 0.004). However, in women who did go on to be diagnosed with breast cancer, there was no overall change in percent density associated with time between first and second mammogram (β = 0.29% per year, p = 0.61); the change over time was statistically significantly different between cases versus controls (p <0.009). If replicated in larger cohorts, these results suggest that within-individual changes in mammographic density as measured by percent density may be a useful biomarker of breast cancer risk.

What effect does mammographic breast density have on lesion detection in digital mammography?

Effective detection of breast cancer using mammography is an important public health issue worldwide. Breasts that contain higher levels of fibroglandular compared with fatty tissue increase breast radio-opacity making it more difficult to differentiate between normal and abnormal findings. The higher prevalence of breast cancer amongst women with denser breasts demands the origination of effective solutions to manage this common radiographic appearance. This brief review considers the impact of higher levels of density on cancer detection and the importance of digital technology in possibly reducing the negative effects of increased density.

Prenatal DES exposure in relation to breast size

PURPOSE

Prenatal DES exposure has been associated with increased risk of breast cancer, but the mechanisms are unknown. Larger bra cup size has also been associated with increased breast cancer risk, although not consistently. We investigated the relation of prenatal DES exposure to mammary gland mass, as estimated by bra cup size.

METHODS

In 2006, 3,222 DES-exposed and 1,463 unexposed women reported their bra cup size, band size (chest circumference), and weight at age 20. Prevalence ratios (PR) were calculated for DES exposure in relation to large bra cup size, with control for year of birth and study cohort. Primary analyses were carried out among women who reported a chest circumference of no more than 32 inches because their cup size would be less influenced by fat mass.

RESULTS

Within this group, DES-exposed women had an estimated 45% increased prevalence (95% CI 0.97-2.18) of large cup size (C or greater) relative to unexposed women. The PR was further increased among women in this group who had a body mass index of < 21 at age 20: PR = 1.83 (95% CI 1.11-3.00). The PR for high-dose DES exposure relative to no exposure was 1.67, 95% CI 1.02-2.73, whereas there was no association of bra cup size with low-dose exposure.

CONCLUSIONS

These results provide support for the hypothesis that in utero DES exposure may result in greater mammary gland mass. Taken together with previous research on bra size and breast cancer risk, these findings suggest a mechanism for a possible association of in utero DES exposure with increased risk of breast cancer.

Mammographic density and risk of breast cancer

The radiographic appearance of the breast on mammography varies among women, and reflects variations in breast tissue composition and the different X-ray attenuation characteristics of these tissues. Fat is radiologically lucent and appears dark on a mammogram. Connective and epithelial tissues are radiologically dense and appear light. These variations in appearance are commonly described as the percentage of the breast image that is radiologically dense, or as percent mammographic density (PMD). There is now extensive evidence that PMD is a risk factor for breast cancer, with a 4- to 6-fold gradient in risk between women with 75% or more PMD compared with those with 10% or less. However, the accuracy of risk prediction in individual women is modest. The extent of PMD is associated inversely with greater age, parity, and weight, and is reduced by the menopause and by tamoxifen. PMD is positively associated with greater height, a family history of breast cancer, and is increased by combined hormone therapy. The relative risk associated with density is substantially larger than the relative risk of breast cancer associated with a family history of the disease or any of the menstrual and reproductive risk factors. It is estimated that the risks of breast cancer attributable to density of 50% or more may be 16% for all breast cancers. Although combined hormone therapy and tamoxifen respectively increase a decrease both PMD and breast cancer risk, there is as yet insufficient evidence to use PMD as a surrogate marker for breast cancer.

Response of bilateral breasts to the endogenous hormonal fluctuation in a menstrual cycle evaluated using 3D MRI

The normal breast tissue responds to the fluctuation of endogenous hormones during a menstrual cycle (MC) and shows changes in breast density. The changes between left and right breasts of the same women were compared to evaluate the symmetrical response. Twenty-four healthy women were recruited in this study. Four weekly magnetic resonance imaging (MRI) studies were performed during one MC. A computer algorithm was used to segment the breast and the fibroglandular tissue to measure the fibroglandular tissue volume (FV) and three morphological parameters: circularity, convexity and irregularity. The coefficient of variation (CV) for each parameter measured among four MRI studies was calculated; also, the maximal percent change between two MRI studies that show the highest and the lowest FV was calculated. These parameters measured from left and right breasts were compared using Pearson correlation. For the FV, the CV measured between left and right breasts of 24 subjects was highly correlated, with r=0.91; the maximal percent difference was also highly correlated, with r=0.93. Overall, the mean left-to-right difference in the measured FV was small: 1.2%±1.1% for CV and 2.6%±2.3% for the maximal percent difference. For the three morphological parameters, the mean left-to-right percentage difference was similar to the differences seen in FV; however, these morphological parameters do not reveal a high functional symmetry between left and right breasts. The results showed that the measured FV from left and right breasts of the same woman revealed a high functional symmetry. Since endogenous hormone plays an important role in the development of breast cancer, it would be interesting to investigate whether the functional asymmetry of response in some patients is associated with the risk of developing unilateral breast cancer.

Mammographic density and breast cancer risk in White and African American Women

Mammographic density is a strong risk factor for breast cancer, but limited data are available in African American (AA) women. We examined the association between mammographic density and breast cancer risk in AA and white women. Cases (n = 491) and controls (n = 528) were from the Carolina Breast Cancer Study (CBCS) who also had mammograms recorded in the Carolina Mammography Registry (CMR). Mammographic density was reported to CMR using Breast Imaging Reporting and Data System (BI-RADS) categories. Increasing mammographic density was associated with increased breast cancer risk among all women. After adjusting for potential confounders, a monotonically increasing risk of breast cancer was observed between the highest versus the lowest BI-RADS density categories [OR = 2.45, (95 % confidence interval: 0.99, 6.09)]. The association was stronger in whites, with ~40 % higher risk among those with extremely dense breasts compared to those with scattered fibroglandular densities [1.39, (0.75, 2.55)]. In AA women, the same comparison suggested lower risk [0.75, (0.30, 1.91)]. Because age, obesity, and exogenous hormones have strong associations with breast cancer risk, mammographic density, and race in the CBCS, effect measure modification by these factors was considered. Consistent with previous literature, density-associated risk was greatest among those with BMI > 30 and current hormone users (P value = 0.02 and 0.01, respectively). In the CBCS, mammographic density is associated with increased breast cancer risk, with some suggestion of effect measure modification by race, although results were not statistically significant. However, exposures such as BMI and hormone therapy may be important modifiers of this association and merit further investigation.

Reproducibility of BI-RADS breast density measures among community radiologists: a prospective cohort study

Using data from the Vermont Breast Cancer Surveillance System (VBCSS), we studied the reproducibility of Breast Imaging Reporting and Data System (BI-RADS) breast density among community radiologists interpreting mammograms in a cohort of 11,755 postmenopausal women. Radiologists interpreting two or more film-screen screening or bilateral diagnostic mammograms for the same woman within a 3- to 24-month period during 1996-2006 were eligible. We observed moderate-to-substantial overall intra-rater agreement for use of BI-RADS breast density in clinical practice, with an overall intra-radiologist percent agreement of 77.2% (95% confidence interval (CI), 74.5-79.5%), an overall simple kappa of 0.58 (95% CI, 0.55-0.61), and an overall weighted kappa of 0.70 (95% CI, 0.68-0.73). Agreement exhibited by individual radiologists varied widely, with intra-radiologist percent agreement ranging from 62.1% to 87.4% and simple kappa ranging from 0.19 to 0.69 across individual radiologists. Our findings underscore the need for additional evaluation of the BI-RADS breast density categorization system in clinical practice.

Outcome of mammography in women with large breasts

Mammography has been established as an effective screening tool for the early detection of breast cancer. Obesity may lead to increased breast size and has been linked to increased rates of breast cancer. As women with larger breasts may be predisposed to developing cancer, it is important that mammography is an appropriate test in these women. This study investigated the sensitivity and specificity of mammography in women with larger breasts in a population screening program.

METHOD

Data was obtained from 848,648 eligible screening episodes of women aged over 40. Of these episodes, 758,860 were eligible for the study, with 7.2% (54,879 screens) deemed to have large breasts. Large breasts were defined as those for whom at least one large cassette was used in the mammographic process. Those women having only four standard cassettes per screen were classified as having average size breasts (703,981 screens, 92.8%). Cancer detection rates, interval cancer rates (false negatives) and recall to assessment rates were compared for women examined on standard sized cassettes versus large cassettes. Chance corrected measures of sensitivity and specificity and 95% confidence intervals (CI) were calculated for women with and without large breasts.

RESULTS

The study found that the sensitivity and specificity of mammography was greater for larger breasted woman. The incidence of breast cancer was also found to be higher in woman with larger breasts in the combined population (73.1 per 100,000 (95% CI 65.9-80.2) in large breasted women versus 52.8 (95% CI 51.1-54.5) in other women) and in each of the specific age groups. This study confirms the appropriateness of mammographic screening for women with large breasts.

Mammographic density and breast cancer risk: current understanding and future prospects

Variations in percent mammographic density (PMD) reflect variations in the amounts of collagen and number of epithelial and non-epithelial cells in the breast. Extensive PMD is associated with a markedly increased risk of invasive breast cancer. The PMD phenotype is important in the context of breast cancer prevention because extensive PMD is common in the population, is strongly associated with risk of the disease, and, unlike most breast cancer risk factors, can be changed. Work now in progress makes it likely that measurement of PMD will be improved in the near future and that understanding of the genetics and biological basis of the association of PMD with breast cancer risk will also improve. Future prospects for the application of PMD include mammographic screening, risk prediction in individuals, breast cancer prevention research, and clinical decision making.

Mammographic density and breast cancer risk: the role of the fat surrounding the fibroglandular tissue

Introduction

Both the percent of mammographic density and absolute dense (fibroglandular) area are strong breast cancer risk factors. The role of non-dense (fat) breast tissue is not often investigated, but we hypothesize that this also influences risk. In this study we investigated the independent effects of dense and fat tissue, as well as their combined effect on postmenopausal breast cancer risk.

Methods

We performed a nested case-control study within the EPIC-NL cohort (358 postmenopausal breast cancer cases and 859 postmenopausal controls). We used multivariate logistic regression analyses to estimate breast cancer odds ratios adjusted for body mass index and other breast cancer risk factors.

Results

Large areas of dense (upper (Q5) vs lower quintile (Q1): OR 2.8 95% CI 1.7 to 4.8) and fat tissue (Q5 vs Q1: OR 2.4; 95% CI 1.3 to 4.2) were independently associated with higher breast cancer risk. The combined measure showed that the highest risk was found in women with both a large (above median) area of dense and fat tissue.

Conclusions

Fibroglandular and breast fat tissue have independent effects on breast cancer risk. The results indicate that the non-dense tissue, which represents the local breast fat, increases risk, even independent of body mass index (BMI). When studying dense breast tissue in relation to breast cancer risk, adjustment for non-dense tissue seems to change risk estimates to a larger extent than adjustment for BMI. This indicates that adjustment for non-dense tissue should be considered when studying associations between dense areas and breast cancer risk.

Nondense mammographic area and risk of breast cancer

Introduction

The mechanisms underlying the strong association between percentage dense area on a mammogram and the risk of breast cancer are unknown. We investigated separately the absolute dense area and the absolute nondense area on mammograms in relation to breast cancer risk.

Methods

We conducted a nested case-control study on prediagnostic mammographic density measurements and risk of breast cancer in the Nurses' Health Study and the Nurses' Health Study II. Premenopausal mammograms were available from 464 cases and 998 controls, and postmenopausal mammograms were available from 960 cases and 1,662 controls. We used a computer-assisted thresholding technique to measure mammographic density, and we used unconditional logistic regression to calculate OR and 95% CI data.

Results

Higher absolute dense area was associated with a greater risk of breast cancer among premenopausal women (OR_{tertile 3 vs 1} = 2.01, 95% CI = 1.45 to 2.77) and among postmenopausal women (OR_{quintile 5 vs 1} = 2.19, 95% CI = 1.65 to 2.89). However, increasing absolute nondense area was associated with a decreased risk of breast cancer among premenopausal women (OR_{tertile 3 vs 1} = 0.51, 95% CI = 0.36 to 0.72) and among postmenopausal women (OR_{quintile 5 vs 1} = 0.46, 95% CI = 0.34 to 0.62). These associations changed minimally when we included both absolute dense area and absolute nondense area in the same statistical model. As expected, the percentage dense area was the strongest risk factor for breast cancer in both groups.

Conclusions

Our results indicate that absolute dense area is independently and positively associated with breast cancer risk, whereas absolute nondense area is independently and inversely associated with breast cancer risk. Since adipose tissue is radiographically nondense, these results suggest that adipose breast tissue may have a protective role in breast carcinogenesis.

Mammographic density and risk of second breast cancer after ductal carcinoma in situ

BACKGROUND

We examined whether mammographic density predicts risk of second breast cancers among patients with ductal carcinoma in situ (DCIS).

METHODS

The study included DCIS patients diagnosed during 1990 to 1997 and treated with breast-conserving surgery at Kaiser Permanente Northern California. Medical records were reviewed for clinical factors and subsequent breast cancers (DCIS and invasive). Ipsilateral mammograms from the index DCIS were assessed for density without knowledge of subsequent cancer status. Cox regression modeling was used to examine the association between mammographic density and risk of breast cancer events.

RESULTS

Of the 935 eligible DCIS patients, 164 (18%) had a subsequent ipsilateral breast cancer, and 59 (6%) had a new primary cancer in the contralateral breast during follow-up (median, 103 mo). Those with the greatest total area of density (upper 20% of values) were at increased risk for invasive disease in either breast [hazard ratio (HR), 2.1; 95% confidence interval (95% CI), 1.2-3.8] or any cancer (DCIS or invasive) in the ipsilateral (HR, 1.7; 95% CI, 1.0-2.9) or contralateral (HR, 3.0; 95% CI, 1.3-6.9) breast compared with those with the smallest area of density (bottom 20%). HRs for these same end points comparing those in the highest with those in the lowest American College of Radiology Breast Imaging Reporting and Data System category were 1.6 (95% CI, 0.7-3.6), 1.3 (95% CI, 0.7-2.6), and 5.0 (95% CI, 1.4-17.9), respectively. There was a suggestion of increasing risk of contralateral, but not ipsilateral, cancer with increasing percent density.

CONCLUSIONS

Women with mammographically dense breasts may be at higher risk of subsequent breast cancer, especially in the contralateral breast.

IMPACT

Information about mammographic density may help with DCIS treatment decisions.

Circulating levels of inflammatory markers and mammographic density among postmenopausal women

Mammographic density is strongly associated with breast cancer risk. Inflammation is involved in breast carcinogenesis, perhaps through effects on mammographic density. We evaluated associations between inflammatory markers interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) and mammographic density among postmenopausal women. Plasma IL-6, TNF-α, and CRP levels were measured in 145 women with benign breast disease (benign controls) and 397 women with a negative screening mammogram (well controls) enrolled in the Mammograms and Masses Study. Associations between the inflammatory markers and mammographic density were evaluated separately for benign and well controls through correlation analyses and linear regressions. Age-adjusted mean CRP levels were higher among benign controls (2.07 μg/ml) compared to well controls (1.63 μg/ml; P = 0.02), while IL-6 and TNF-α levels were similar between groups. Using linear regression, IL-6, TNF-α, and CRP were not statistically significantly associated with dense breast area within either group. Statistically significant positive associations were observed between all three markers and nondense breast area in both groups; statistically significant negative associations were observed between IL-6 and percent density among benign controls, and between all three markers and percent density among well controls. These associations were all attenuated and non-significant upon adjustment for body mass index. IL-6, TNF-α, and CRP levels were not independently associated with dense breast area, nondense breast area, or percent density in this study population. Our results suggest that these inflammatory factors do not impact breast carcinogenesis through independent effects on mammographic density.

Breast tissue composition and susceptibility to breast cancer

Breast density, as assessed by mammography, reflects breast tissue composition. Breast epithelium and stroma attenuate x-rays more than fat and thus appear light on mammograms while fat appears dark. In this review, we provide an overview of selected areas of current knowledge about the relationship between breast density and susceptibility to breast cancer. We review the evidence that breast density is a risk factor for breast cancer, the histological and other risk factors that are associated with variations in breast density, and the biological plausibility of the associations with risk of breast cancer. We also discuss the potential for improved risk prediction that might be achieved by using alternative breast imaging methods, such as magnetic resonance or ultrasound. After adjustment for other risk factors, breast density is consistently associated with breast cancer risk, more strongly than most other risk factors for this disease, and extensive breast density may account for a substantial fraction of breast cancer. Breast density is associated with risk of all of the proliferative lesions that are thought to be precursors of breast cancer. Studies of twins have shown that breast density is a highly heritable quantitative trait. Associations between breast density and variations in breast histology, risk of proliferative breast lesions, and risk of breast cancer may be the result of exposures of breast tissue to both mitogens and mutagens. Characterization of breast density by mammography has several limitations, and the uses of breast density in risk prediction and breast cancer prevention may be improved by other methods of imaging, such as magnetic resonance or ultrasound tomography.

Mammographic density change with 1 year of aerobic exercise among postmenopausal women: a randomized controlled trial

BACKGROUND

The Alberta Physical Activity and Breast Cancer Prevention (ALPHA) Trial examined the influence of aerobic exercise on biological factors that are associated with breast cancer risk. Mammographic density, a secondary outcome, is reported here.

METHODS

The ALPHA Trial was a parallel group randomized controlled trial conducted between May 2003 and July 2007. Postmenopausal, sedentary women ages 50 to 74 years (n = 320) were evenly randomized to aerobic exercise (45 minutes, 5 days per week) or control (usual life-style) for 1 year. Dense fibroglandular tissue and nondense fatty tissue were measured from mammograms at baseline and 1 year using computer-assisted thresholding software for area measurements and a new technique that relies on the calibration of mammography units with a tissue-equivalent phantom for volumetric measurements.

RESULTS

Nondense volume decreased in the exercise group relative to the control group (difference between groups = -38.5 cm(3); 95% confidence interval, -61.6 to 15.4; P = 0.001). Changes in total body fat accounted for this decrease. Changes in dense area and dense volume, measures that have previously been associated with breast cancer risk, were not significantly different between the groups (P > or = 0.26).

CONCLUSIONS

Achieving changes in mammographic measures may require more exercise or a study population with higher baseline levels of sex hormones or a wider range of mammographic density. The data from this study, however, suggest that the protective effect of exercise on breast cancer risk may operate through a mechanism other than mammographic density.

Determinants of percentage and area measures of mammographic density

Mammographic density is one of the strongest predictors of breast cancer risk. Typically expressed as a percentage of the breast area occupied by radiologically dense tissue on a mammogram, its full value may not be realized because of its negative association with body mass index. A simpler measure of mammographic density, independent of other breast cancer risk factors and equally predictive of risk, would be preferable for risk prediction models. Percentage and area measures of mammographic density were determined for 815 women at high risk for breast cancer from the baseline assessments in the International Breast Cancer Intervention Study I, a trial of tamoxifen for breast cancer prevention conducted between 1992 and 2001. Multivariate linear regression was used to assess associations between risk factors and the mammographic measures. Percent dense area was negatively associated with age, body mass index, menopausal status, predicted risk, and smoking status (R(2) = 24%). Dense area was negatively associated with only age and body mass index (R(2) = 7%), and the latter association was much weaker than for percent dense area. Nondense area was positively associated with age, body mass index, and predicted risk (R(2) = 36%). Dense area was not associated with the multitude of risk factors that percent dense area was, making it a simpler biomarker for risk prediction modeling. Both dense area and percent dense area should be presented whenever possible for comparisons in research.

Exogenous and endogenous hormones, mammographic density and breast cancer risk: can mammographic density be considered an intermediate marker of risk?

Elevated mammographic density measures are a well-established, relatively strong risk factor for breast cancer development. A systematic review of prospective cohort studies and cross-sectional studies strikingly establishes parallels between the associations of combined postmenopausal estrogen and progestin replacement therapy with, on the one hand, mammographic densities and, on the other hand, breast cancer risk. Other parallel observations were the inverse associations of both mammographic density and breast cancer risk with the selective estrogen receptor modulator tamoxifen, and direct associations with prolactin. Paradoxically, however, high mammographic density has been found associated with higher risks of both estrogen- and progesterone-receptor positive (ER+/ PR+) and negative (ER-/PR-) breast cancers, while hormone replacement therapy (HRT) use, but also circulating (blood) levels of androgens, estrogens, and prolactin appear to be associated more specifically to the risk of ER+ tumors. The effects of aromatase inhibitors and gonadotropin-releasing hormone agonists on breast density, as well as on breast cancer risk, still require further investigation. Regarding circulating levels of insulin-like growth factor (IGF)-I or IGFBP-3, studies did not show fully consistent relationships with mammographic density measures and breast cancer risk. In view of these various findings, it is impossible, at present, to propose mammographic density measures as an intermediate risk-related phenotype, integrating the effects of exogenous and/or endogenous hormones on the risk of developing breast cancer.

Mammographic breast density--evidence for genetic correlations with established breast cancer risk factors

Previous twin and family studies indicate that the familial aggregation of breast density is due (in part) to genetic factors. Whether these genetic influences are shared with other breast cancer risk factors, however, is not known. Using standard film-screen mammography, we screened 550 women, including 611 pairs of sisters, from the Old Order Amish population of Lancaster County, Pennsylvania. We digitized mammograms and quantified the dense and nondense areas of the breast using a computer-assisted method. Information about other breast cancer risk factors was collected via questionnaires and a physical exam. Using pedigree-based variance component methods, we estimated the genetic contributions to several breast cancer risk factors, including breast density, and evaluated the evidence for shared genetic influences between them. After adjusting for covariates, genetic effects accounted for >33% of the total variance of each risk factor (P < 0.001), including breast density, and the dense and nondense areas of the breast were significantly genetically correlated with parity [genetic correlation (rho(G)) = -0.47; P = 0.013] and age at menarche (rho(G) = -0.38; P = 0.008), respectively. The nondense area of the breast and, in turn, breast density, expressed as a ratio of dense area to total area, were also genetically correlated with most measures of adiposity but in opposite directions (rho(G) > or = 0.75; P < 10(-7) for nondense area). We conclude that the genetic components that influence breast density are not independent of the genetic components that influence other breast cancer risk factors. This shared genetic architecture should be considered in future genetic studies of breast density.

Association between transillumination breast spectroscopy and quantitative mammographic features of the breast

Transillumination breast spectroscopy (TiBS) uses nonionizing optical radiation to gain information about tissue properties directly from the breast. TiBS measurements were obtained from 225 women with normal mammograms. Principal component analysis was used to reduce the spectral data set into four principal components and to generate four TiBS scores (t1-t4) for each woman. These components and scores represent light scattering, water, lipid, and hemoglobin content. Percent density, dense area, and nondense area were measured using Cumulus. The association between TiBS scores and quantitative mammographic features was analyzed using linear regression stratified by menopausal status and adjusted for body mass index. Among premenopausal women, t1 and t3 were significantly associated with percent density (beta t1 = -0.14, P = 0.04; beta t3 = -2.43, P < 0.0001), whereas t2 and t3 were significantly associated with dense area (beta t2 = -1.57, P < 0.0001; beta t3 = -2.54, P < 0.0001). Among postmenopausal women, t1, t3, and t4 were significantly associated with percent density (beta t1 = -0.30, P < 0.0001; beta t3 = -2.51, P < 0.0001; beta t4 = 4.75, P < 0.0001) and dense area (beta t1 = -0.19, P = 0.004; beta t3 = -2.13, P = 0.002; beta t4 = 5.02, P < 0.0001). Scores t2 and t4 were also significantly correlated with age among postmenopausal women (rt2 = 0.41 and rt4 = -0.36). Given the association with quantitative mammographic features and tissue changes related to age and menopause, TiBS scores may prove useful as intermediate markers in studies of breast cancer etiology and prevention.